Strong and weak constraint variational assimilations for reduced order fluid flow modeling
- Autores
- Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.
- Año de publicación
- 2012
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data.
Fil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cammilleri, A.. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Carlier, J.. No especifíca;
Fil: Mémin, E.. Institut National de Recherche en Informatique et en Automatique; Francia - Materia
-
PIV
POD
REDUCED ORDER DYNAMICAL SYSTEMS
VARIATIONAL ASSIMILATION
WAKE FLOW - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/195205
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Strong and weak constraint variational assimilations for reduced order fluid flow modelingArtana, Guillermo OsvaldoCammilleri, A.Carlier, J.Mémin, E.PIVPODREDUCED ORDER DYNAMICAL SYSTEMSVARIATIONAL ASSIMILATIONWAKE FLOWhttps://purl.org/becyt/ford/2.3https://purl.org/becyt/ford/2In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data.Fil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cammilleri, A.. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Carlier, J.. No especifíca;Fil: Mémin, E.. Institut National de Recherche en Informatique et en Automatique; FranciaAcademic Press Inc Elsevier Science2012-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/195205Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.; Strong and weak constraint variational assimilations for reduced order fluid flow modeling; Academic Press Inc Elsevier Science; Journal of Computational Physics; 231; 8; 4-2012; 3264-32880021-9991CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021999112000319info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jcp.2012.01.010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:38:40Zoai:ri.conicet.gov.ar:11336/195205instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:38:41.163CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
title |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
spellingShingle |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling Artana, Guillermo Osvaldo PIV POD REDUCED ORDER DYNAMICAL SYSTEMS VARIATIONAL ASSIMILATION WAKE FLOW |
title_short |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
title_full |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
title_fullStr |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
title_full_unstemmed |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
title_sort |
Strong and weak constraint variational assimilations for reduced order fluid flow modeling |
dc.creator.none.fl_str_mv |
Artana, Guillermo Osvaldo Cammilleri, A. Carlier, J. Mémin, E. |
author |
Artana, Guillermo Osvaldo |
author_facet |
Artana, Guillermo Osvaldo Cammilleri, A. Carlier, J. Mémin, E. |
author_role |
author |
author2 |
Cammilleri, A. Carlier, J. Mémin, E. |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
PIV POD REDUCED ORDER DYNAMICAL SYSTEMS VARIATIONAL ASSIMILATION WAKE FLOW |
topic |
PIV POD REDUCED ORDER DYNAMICAL SYSTEMS VARIATIONAL ASSIMILATION WAKE FLOW |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.3 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data. Fil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cammilleri, A.. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina Fil: Carlier, J.. No especifíca; Fil: Mémin, E.. Institut National de Recherche en Informatique et en Automatique; Francia |
description |
In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system’s coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-04 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/195205 Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.; Strong and weak constraint variational assimilations for reduced order fluid flow modeling; Academic Press Inc Elsevier Science; Journal of Computational Physics; 231; 8; 4-2012; 3264-3288 0021-9991 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/195205 |
identifier_str_mv |
Artana, Guillermo Osvaldo; Cammilleri, A.; Carlier, J.; Mémin, E.; Strong and weak constraint variational assimilations for reduced order fluid flow modeling; Academic Press Inc Elsevier Science; Journal of Computational Physics; 231; 8; 4-2012; 3264-3288 0021-9991 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021999112000319 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jcp.2012.01.010 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Academic Press Inc Elsevier Science |
publisher.none.fl_str_mv |
Academic Press Inc Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614410414325760 |
score |
13.070432 |